The rest of the paper is organized as follows. In the first part of the paper two quantization strategies for the feedback are proposed: one based on the nested dropout (ND) layer [1]} and a second one based on the vector quantizer variational AE (VQ-VAE) [2]}.
With the first approach, we can define an ordering of the latent space units. Therefore, when only a few bits are available for the latent space quantization, we can distribute more bits to the most important units to achieve high reconstruction accuracy. With the second approach, the codebook is found during the training of the AE and the benefits of vector quantization are exploited.
In the second part, we focus our attention on the reduction of both the number of parameters to be offloaded to the MT, and the complexity of the deep NN in terms of number of floating point operations (\(\#\) FLOPs), for the considered AE architecture [3]}.
